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Table 1 Allelic richness estimated by regression, coalescent and rarefaction

From: A simple method for estimating genetic diversity in large populations from finite sample sizes

Species ID Source data set Estimated allelic richness
   No. of loci N A Subsampling
(n = 120)
ρ
(n = 120)
θ Ewens
(n = 120)
θ coalescent
(n = 120)
Rarefaction
(n = 120)
Microsatellites
Picea rubens PR1 6 180 13.00 11.06 11.04 11.98 9.23 10.68
  PR2 6 180 13.33 11.18 11.17 12.29 8.94 10.71
  PR3 6 180 15.33 12.48 12.44 14.13 11.92 12.19
  PR4 6 180 14.83 12.48 12.44 13.67 12.13 11.92
Picea glauca PG1 6 105 22.83 21.13 21.30 23.49 35.74 20.96
  PG2 6 105 22.83 20.55 20.62 23.49 51.84 20.44
Pinus strobus PS1 13 102 9.77 9.03 9.13 10.11 17.57 9.03
  PS2 13 102 9.23 8.67 8.73 9.55 15.91 8.68
Thuja occidentalis TO1 6 100 7.83 7.18 7.17 8.14 12.26 7.17
  TO2 6 100 9.67 8.95 9.00 10.05 16.28 9.09
  TO3 6 100 8.83 7.86 7.95 9.18 14.06 7.95
Allozymes
Pinus strobus PS1 15 95 3.20 2.97 2.98 3.38 3.34 2.93
  PS2 15 95 3.27 3.09 3.10 3.59 4.15 3.04
  1. Subsampling - allelic richness estimated by repeated random subsampling in pseudosimulated population data sets based on the empirical data. ρ - allelic richness predicted by the regression model (5). θEwens - Allelic richness predicted by the Ewens sampling formula (3), where θ was directly calculated from the empirical data set. θcoalescent - Allelic richness predicted by the Ewens sampling formula (3), where θ was estimated by coalescent approach from the empirical data set. Rarefaction - Allelic richness predicted by rarefaction of the source empirical data set to the sample size of n = 120.